Diagnostic accuracy of frozen sections for intraoperative detection of spread through air spaces in early-stage lung cancer: Systematic review and meta-analysis.

e20062 Background: Tumor spread through air spaces (STAS) is an independent predictor of recurrence and lung cancer–specific mortality in early-stage lung cancer. Intraoperative detection of STAS using frozen section analysis may aid surgical decision-making by guiding appropriate selection between lobectomy and sub-lobar resection. Methods: We systematically searched five databases from inception to October 2025 to identify PICO-concordant studies evaluating the diagnostic accuracy of intraoperative frozen section analysis for detecting STAS, using permanent paraffin sections as the reference standard. Pooled sensitivity, specificity, SROC curves, and diagnostic odds ratios were estimated using Bayesian bivariate hierarchical and random-effects models. Heterogeneity, sensitivity analyses, and publication bias were assessed in R (v2024.12.0+467) using the meta and mada packages. Results: Using a Bayesian bivariate hierarchical model, frozen section analysis demonstrated a pooled sensitivity of 0.66 (95% credible interval [CrI], 0.51–0.79) and specificity of 0.87 (95% CrI, 0.68–0.95), with a diagnostic odds ratio (DOR) of 12.74 (95% CrI, 1.86–23.63). Substantial heterogeneity was observed (I²E sensitivity: 0.82 [0.74–0.89]; I²E specificity: 0.72 [0.47–0.96]), with between-study variance estimates of 0.74 for logit sensitivity and 2.80 for logit specificity. In univariate analyses, pooled sensitivity was 0.60 (95% CI, 0.49–0.70; I² = 65.6%) and specificity was 0.87 (95% CI, 0.65–0.96; I² = 93.5%), yielding a DOR of 13.90 (95% CI, 5.95–32.49; I² = 88.6%). Heterogeneity remained stable on leave-one-out and subgroup analyses. Deeks’ funnel plot showed no evidence of publication bias (p = 0.62). Conclusions: Frozen section analysis demonstrates relatively high specificity but moderate sensitivity for STAS detection, making it more suitable as a rule-in rather than a rule-out test. Positive intraoperative findings may support escalation to lobectomy, whereas negative results should be interpreted with caution.

Authors

Institutions

Publication Details

Journal
Journal of Clinical Oncology
Published
2026-05-28
DOI
https://doi.org/10.1200/jco.2026.44.16_suppl.e20062
Primary Topic
Lung Cancer Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Diagnostic accuracy of frozen sections for intraoperative detection of spread through air spaces in early-stage lung cancer: Systematic review and meta-analysis.

Abdullah Jehangir, Muhammad Arham, Hamza Hameed, Muhammad Uzair et al.
Journal of Clinical Oncology
Lung Cancer Diagnosis and Treatment
article

Diagnostic accuracy of frozen sections for intraoperative detection of spread through air spaces in early-stage lung cancer: Systematic review and meta-analysis.

Abdullah Jehangir, Muhammad Arham, Hamza Hameed, Muhammad Uzair, Hanzala Jehangir, Deevyashali Parekh, Abdel-Azez Abu-Samak, M Pustake, Manahil Shafique, Aquiles De Leon Javier, Gerardo Capo
article en

Abstract

e20062 Background: Tumor spread through air spaces (STAS) is an independent predictor of recurrence and lung cancer–specific mortality in early-stage lung cancer. Intraoperative detection of STAS using frozen section analysis may aid surgical decision-making by guiding appropriate selection between lobectomy and sub-lobar resection. Methods: We systematically searched five databases from inception to October 2025 to identify PICO-concordant studies evaluating the diagnostic accuracy of intraoperative frozen section analysis for detecting STAS, using permanent paraffin sections as the reference standard. Pooled sensitivity, specificity, SROC curves, and diagnostic odds ratios were estimated using Bayesian bivariate hierarchical and random-effects models. Heterogeneity, sensitivity analyses, and publication bias were assessed in R (v2024.12.0+467) using the meta and mada packages. Results: Using a Bayesian bivariate hierarchical model, frozen section analysis demonstrated a pooled sensitivity of 0.66 (95% credible interval [CrI], 0.51–0.79) and specificity of 0.87 (95% CrI, 0.68–0.95), with a diagnostic odds ratio (DOR) of 12.74 (95% CrI, 1.86–23.63). Substantial heterogeneity was observed (I²E sensitivity: 0.82 [0.74–0.89]; I²E specificity: 0.72 [0.47–0.96]), with between-study variance estimates of 0.74 for logit sensitivity and 2.80 for logit specificity. In univariate analyses, pooled sensitivity was 0.60 (95% CI, 0.49–0.70; I² = 65.6%) and specificity was 0.87 (95% CI, 0.65–0.96; I² = 93.5%), yielding a DOR of 13.90 (95% CI, 5.95–32.49; I² = 88.6%). Heterogeneity remained stable on leave-one-out and subgroup analyses. Deeks’ funnel plot showed no evidence of publication bias (p = 0.62). Conclusions: Frozen section analysis demonstrates relatively high specificity but moderate sensitivity for STAS detection, making it more suitable as a rule-in rather than a rule-out test. Positive intraoperative findings may support escalation to lobectomy, whereas negative results should be interpreted with caution.

Journal of Clinical OncologyVol. 44(16_suppl)
Rutgers, The State University of New Jersey (US), Texas Tech University (US), College of Saint Elizabeth (US), SUNY Upstate Medical University (US), Bahawal Victoria Hospital (PK), Henry Ford Hospital (US), Shaikh Zayed Medical College and Hospital (PK), Trinitas Regional Medical Center (US), Texas Tech University Health Sciences Center (US)
Good health and well-being
Openalex Percentile: Top 6%
Lung Cancer Diagnosis and Treatment
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.